A Multi-Expert Classification Framework for Network Misuse Detection

Keywords

Abstract

The Internet has provided remarkable advantages for data
communication between computers. However, its
complexity and openness as well as rapidly evolving
technologies have posed computer networks to a major
challenge in terms of protecting the security and privacy
of information as it is transmitted from one place to
another. Therefore, it is critical to make computing safe
and secure from malicious hackers and viruses. The
current existing methods suffer from low accuracy and
system robustness. To overcome such limitations, this
paper proposes a multi-expert classification framework
for detecting different types of network anomalies.
Specifically, different types of intrusions will be detected
with different strategies, including different encoding
schemes, attribute selections and learning algorithms. The
Knowledge Discovery and Data Mining (KDD-99)
dataset is used as a benchmark to compare this method
with other existing techniques. It is empirically shown
that the proposed design outperforms other state-of-the-art
learning methods in terms of learning bias and
generalization variance.